Quantum Information Processing最新文献

With the advancement of quantum computing, symmetric cryptography faces new challenges from quantum attacks. These attacks are typically classified into two models: Q1 (classical queries) and Q2 (quantum superposition queries). In this context, we present a comprehensive security analysis of the FBC algorithm considering quantum adversaries with different query capabilities. In the Q2 model, we first design 4-round polynomial-time quantum distinguishers for FBC-F and FBC-KF structures, and then perform (r(r>6))-round quantum key-recovery attacks. Our attacks require (O(2^{(2n(r-6)+3n)/2})) quantum queries, reducing the time complexity by a factor of (2^{4.5n}) compared with quantum brute-force search, where n denotes the subkey length. Moreover, we give a new 6-round polynomial-time quantum distinguisher for FBC-FK structure. Based on this, we construct an (r(r>6))-round quantum key-recovery attack with complexity (O(2^{n(r-6)})). Considering an adversary with classical queries and quantum computing capabilities, we demonstrate low-data quantum key-recovery attacks on FBC-KF/FK structures in the Q1 model. These attacks require only a constant number of plaintext-ciphertext pairs, then use the Grover algorithm to search the intermediate states, thereby recovering all keys in (O(2^{n/2})) time.

Defect detection in castings is essential for ensuring structural reliability in quality-critical industries. Traditional deep learning approaches face challenges such as high computational cost, susceptibility to noise, and overfitting when trained on limited datasets. To address these issues, we propose a hybrid quantum–classical framework for automated casting defect detection using quantum-inspired neural networks (QNNs). Here, “quantum-inspired” refers to algorithms based on principles of quantum walks and variational quantum circuits, implemented on classical simulation hardware. The model combines classical preprocessing with quantum variational layers to classify defects such as porosity, shrinkage, and micro-cracks from X-ray images. Experiments on the GDXray Castings dataset show that the eight-qubit QNN achieved 93.8% accuracy, 92.7% precision, 94.5% recall, and an F1-score of 93.6%, surpassing a baseline convolutional neural network. This work provides the first reported use of QNNs for casting inspection, offering a promising pathway toward robust and scalable non-destructive testing solutions in smart manufacturing.

This paper proposes a new model, alternating quantum walk with potentials (AQWP), designed to solve high-dimensional non-convex optimization problems. The method integrates problem-dependent potential-induced phase modulation into an alternating discrete-time quantum walk, enabling directional interference bias toward descent directions while preserving coherent quantum dynamics. A formal analysis of the algorithmic mechanism demonstrates that potential-induced phases generate constructive interference along descent paths and destructive interference elsewhere, with finite potential barriers traversable via quantum tunneling. Under mild regularity assumptions, this yields probabilistic concentration near low-energy regions instead of trapping at local minima. Computational complexity analysis of AQWP, accounting for classical preprocessing and quantum evolution, shows the overall cost scales polynomially with problem dimension and iteration count. To address parameter sensitivity, an online local estimation strategy for the phase normalization parameter is introduced, revealing a broad robustness interval that obviates global landscape scanning. Extensive numerical experiments on benchmark non-convex functions and binary classification neural networks confirm AQWP’s stability under random initialization and favorable scaling with input dimension and network capacity. Compared with classical baselines, AQWP consistently achieves faster convergence and better solution quality, establishing it as a scalable, robust quantum-inspired optimization paradigm for non-convex learning tasks.

We present an iterative algorithm based on semidefinite programming (SDP) for computing the quantum smooth max-mutual information (I^varepsilon _{max }(rho _{AB})) of bipartite quantum states in any dimension. The algorithm is accurate if a rank condition for marginal states within the smoothing environment is satisfied and provides an upper bound otherwise. Central to our method is a novel SDP, for which we establish primal and dual formulations and prove strong duality. With the direct application of bounding the one-shot distillable key of a quantum state, this contribution extends SDP-based techniques in quantum information theory. Thereby it improves the capabilities to compute or estimate information measures with application to various quantum information processing tasks.

Quantum key distribution (QKD) entanglement-based solutions have become effective solutions for providing security to communications owing to the increased challenges posed by quantum computing. Entanglement-based QKD protocols are especially suitable for deployment over existing metropolitan optical fibre infrastructure, particularly those that provide device-independent security. Nonetheless, their implementation is strongly hindered by environmental noise, decoherence by optical fibres, and interference by co-existent classical data traffic. This research paper provides an overall experimental report on the noise resistance of entanglement-based QKD, in this case, the BBM92 protocol of a metropolitan fibre network. To solve practical problems, this study presents a hybrid model with three adaptive algorithms: the Adaptive entanglement purification protocol (AEPP), noise-resilient key reconciliation algorithm (NRKRA), and optimised privacy amplification technique (OPAT). The AEPP constantly checks the entanglement fidelity and enhances it through the purification of pairs of photons, depending on the real-time channel conditions. The NRKRA builds upon efficient key reconciliation by dynamically using error correction coded to optimise the observed quantum bit error rate (QBER). OPAT also enhances system security because it can handle privacy amplification based on the degree of information leakage. Experimental findings indicate that such a combined method is able to produce low QBER, high secret key rates with improved entanglement fidelity, even in the fibre links of up to 50 km length and in the presence of environmental experiments. This work has shown that an adaptable and resilient structure of this kind goes far beyond the conceptual frameworks of QKD modelling and real-world applications to provide insights on the necessary performance standards and to provide viable implementation guidelines to large-scale quantum cryptographic systems in the future.

Public-key cryptosystems such as RSA rely on the classical intractability of integer factorization, which is threatened by Shor’s quantum algorithm. While theoretically efficient, practical implementations face significant challenges due to noise in current quantum devices. This paper introduces an information-theoretic framework based on von Neumann entropy to analyze the robustness of Shor’s period-finding subroutine under various noise models. Through simulations of small moduli ((N=15,21)) under depolarizing, amplitude damping, and phase damping channels, this study demonstrates that entropy growth strongly correlates with success probability degradation, identify critical entropy thresholds marking the collapse of period finding, and report a robustness hierarchy, amplitude damping > phase damping > depolarizing noise. These findings provide hardware-agnostic insights for cryptanalytic security assessment and error mitigation strategies.

Various approaches have been proposed to enable quantum generative adversarial networks (QGANs) to learn data distributions and generate images similar to original data. While QGANs are theoretically capable of modeling high-dimensional, complex distributions with fewer parameters and shorter training times than classical GANs, current hardware limitations hinder their performance. As a result, research has increasingly focused on identifying specific domains where QGANs can offer unique advantages. In our prior work, we proposed QryptGen, a PQWGAN-based framework for generating 28 (times ) 28 grayscale encryption key images from chaotic data. This demonstrated that QGANs can learn from visually indistinct, high-entropy distributions—beyond conventional datasets like MNIST—and produce image-based keys suitable for secure domains such as military communication, medical diagnostics, and cloud privacy systems. However, QryptGen’s use of row-wise patch stacking introduced inter-row correlations, reducing the randomness of the generated keys. Moreover, the PQWGAN loss function, optimized for structured data, was insufficient to fully capture the irregularity of chaotic distributions. To address these issues, we introduce QryptGen+, a redesigned framework that enhances randomness in key image generation. Key improvements include larger patch-wise generation to reduce structural bias, a strongly entangling quantum layer, a balanced training ratio between the generator and the critic, and a novel loss function that promotes anti-correlation and maximizes entropy. Through these refinements, QryptGen+ yields encryption keys with higher statistical security, reaffirming the potential of quantum machine learning for cryptographic applications.

This paper investigates the thermodynamic performance of a quantum heat engine (QHE) based on a three-spin-½ XX model in the presence of Dzyaloshinskii–Moriya (DM) interaction and an external magnetic field. By analyzing the system under varying DM coupling parameters and magnetic field strengths, we calculate the heat absorbed from the hot reservoir, heat released to the cold reservoir, net work output, and cycle efficiency. The results show that optimal engine performance, characterized by high positive work and efficiency, occurs when there is a significant asymmetry between the DM interaction strengths in the hot and cold stages of the cycle. In the absence of a magnetic field, a large D₁ and small D₂ enhance work extraction and efficiency. Introducing an external magnetic field modifies the energy spectrum through Zeeman splitting, leading to nontrivial behaviors such as isolated regions of positive work and reduced maximum efficiency. At strong field strengths, the interplay between DM interaction and the magnetic field becomes the dominant factor determining work output and efficiency. These findings highlight the sensitivity of spin-based quantum heat engines to interaction parameters and external control fields, offering insights into the design of efficient nanoscale thermal machines.

The rapid development of quantum computing poses a serious threat to traditional encryption techniques, especially in the field of image security, where traditional encryption schemes are difficult to resist quantum attacks. Therefore, research on quantum image encryption is crucial. In the research of quantum image encryption, the high randomness and high security of quantum keys have become the core elements to ensure system robustness. Therefore, in order to provide high-quality quantum keys, this study is inspired by low-dimensional quantum walk and proposes two quantum key generation schemes with high randomness: one is to construct a high-dimensional quantum walk paradigm using the physical properties of quantum walk; the second is to enhance the randomness of the basic quantum key by introducing an entropy-increasing circuit. On this basis, in order to verify the randomness and security of the key, a quantum walk-based scrambling algorithm and a shift-overlap XOR diffusion algorithm were designed, and corresponding quantum circuit implementations were provided. Finally, the proposed method was validated through security experiments. The experimental results show that the average entropy of the encrypted information is 7.9991, which is close to the ideal value, indicating that the key has good randomness. The robustness test results show that the ciphertext can effectively resist salt, pepper, and noise attacks. Meanwhile, the analysis of plaintext sensitivity shows that the NPCR value is 99.5431 and the UACI value is 33.5228, both of which meet the requirements of secure encryption systems.

To address the quantum resource bottleneck in conventional quantum secret sharing (QSS) and meet hierarchical security demands in multi-level quantum networks, this paper proposes a Hybrid Quantum State Sharing (HQSS) scheme that integrates quantum compression with classical threshold sharing. The protocol combines the Multi-scale Entanglement Renormalization Ansatz (MERA) for hierarchical quantum state compression with Shamir’s (t, n) threshold scheme. Quantum states are compressed through disentanglement/isometry hierarchies, reducing l-level states to single qubits, while corresponding operation sequences are encoded as classical shares. The framework supports collaborative reconstruction requiring only one quantum participant and l classical participants. This work resolves scalability challenges in quantum networks through MERA-based compression and hybrid quantum–classical architecture. The scheme provides a practical solution for hierarchical quantum state sharing.